-
Notifications
You must be signed in to change notification settings - Fork 6
/
licence.py
219 lines (188 loc) · 7.95 KB
/
licence.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
import sys
sys.path.append('./LPRNet')
sys.path.append('./MTCNN')
from PyQt5.QtWidgets import QProgressBar
from LPRNet_Test import *
from MTCNN import *
import numpy as np
import torch
import time
import cv2
import torch.nn as nn
import torch
#from yolo import YOLO
from PIL import Image
import numpy as np
import cv2
from utils.utils import plot_one_box
import time
import re
import csv
import shutil
class small_basic_block(nn.Module):
def __init__(self, ch_in, ch_out):
super(small_basic_block, self).__init__()
self.block = nn.Sequential(
nn.Conv2d(ch_in, ch_out // 4, kernel_size=1),
nn.ReLU(),
nn.Conv2d(ch_out // 4, ch_out // 4, kernel_size=(3, 1), padding=(1, 0)),
nn.ReLU(),
nn.Conv2d(ch_out // 4, ch_out // 4, kernel_size=(1, 3), padding=(0, 1)),
nn.ReLU(),
nn.Conv2d(ch_out // 4, ch_out, kernel_size=1),
)
def forward(self, x):
return self.block(x)
class LPRNet(nn.Module):
def __init__(self, class_num, dropout_rate):
super(LPRNet, self).__init__()
self.class_num = class_num
self.backbone = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1), # 0
nn.BatchNorm2d(num_features=64),
nn.ReLU(), # 2
nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 1, 1)),
small_basic_block(ch_in=64, ch_out=128), # *** 4 ***
nn.BatchNorm2d(num_features=128),
nn.ReLU(), # 6
nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(2, 1, 2)),
small_basic_block(ch_in=64, ch_out=256), # 8
nn.BatchNorm2d(num_features=256),
nn.ReLU(), # 10
small_basic_block(ch_in=256, ch_out=256), # *** 11 ***
nn.BatchNorm2d(num_features=256), # 12
nn.ReLU(), #13
nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(4, 1, 2)), # 14
nn.Dropout(dropout_rate),
nn.Conv2d(in_channels=64, out_channels=256, kernel_size=(1, 4), stride=1), # 16
nn.BatchNorm2d(num_features=256),
nn.ReLU(), # 18
nn.Dropout(dropout_rate),
nn.Conv2d(in_channels=256, out_channels=class_num, kernel_size=(13, 1), stride=1), # 20
nn.BatchNorm2d(num_features=class_num),
nn.ReLU(), # *** 22 ***
)
self.container = nn.Sequential(
nn.Conv2d(in_channels=256+class_num+128+64, out_channels=self.class_num, kernel_size=(1,1), stride=(1,1)),
# nn.BatchNorm2d(num_features=self.class_num),
# nn.ReLU(),
# nn.Conv2d(in_channels=self.class_num, out_channels=self.lpr_max_len+1, kernel_size=3, stride=2),
# nn.ReLU(),
)
def forward(self, x):
keep_features = list()
for i, layer in enumerate(self.backbone.children()):
x = layer(x)
if i in [2, 6, 13, 22]: # [2, 4, 8, 11, 22]
#print("intermediate feature map {} shape is: ".format(i), x.shape)
keep_features.append(x)
global_context = list()
for i, f in enumerate(keep_features):
if i in [0, 1]:
f = nn.AvgPool2d(kernel_size=5, stride=5)(f)
if i in [2]:
f = nn.AvgPool2d(kernel_size=(4, 10), stride=(4, 2))(f)
f_pow = torch.pow(f, 2)
f_mean = torch.mean(f_pow)
f = torch.div(f, f_mean)
#print("after globel context {} shape is: ".format(i), f.shape)
global_context.append(f)
x = torch.cat(global_context, 1)
x = self.container(x)
#print("after container shape is: ", x.shape)
logits = torch.mean(x, dim=2)
return logits
class Licence(object):
def __init__(self):
super().__init__()
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#self.yolo = YOLO()
self.STN = STNet()
self.STN.to(self.device)
self.STN.load_state_dict(torch.load('LPRNet/weights/Final_STN_model.pth', map_location=lambda storage, loc: storage))
self.STN.eval()
self.lprnet = LPRNet(class_num=len(CHARS), dropout_rate=0)
self.lprnet.to(self.device)
self.lprnet.load_state_dict(torch.load('LPRNet/weights/Final_LPRNet_model.pth', map_location=lambda storage, loc: storage))
self.lprnet.eval()
self.CHARS = ['京', '沪', '津', '渝', '冀', '晋', '蒙', '辽', '吉', '黑',
'苏', '浙', '皖', '闽', '赣', '鲁', '豫', '鄂', '湘', '粤',
'桂', '琼', '川', '贵', '云', '藏', '陕', '甘', '青', '宁',
'新',
'0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K',
'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',
'W', 'X', 'Y', 'Z', 'I', 'O', '-'
]
def convert_image(self,inp):
# convert a Tensor to numpy image
inp = inp.squeeze(0).cpu()
inp = inp.detach().numpy().transpose((1,2,0))
inp = 127.5 + inp/0.0078125
inp = inp.astype('uint8')
return inp
def decode(self,preds, CHARS):
# greedy decode
pred_labels = list()
labels = list()
for i in range(preds.shape[0]):
pred = preds[i, :, :]
pred_label = list()
for j in range(pred.shape[1]):
pred_label.append(np.argmax(pred[:, j], axis=0))
no_repeat_blank_label = list()
pre_c = pred_label[0]
for c in pred_label: # dropout repeate label and blank label
if (pre_c == c) or (c == len(CHARS) - 1):
if c == len(CHARS) - 1:
pre_c = c
continue
no_repeat_blank_label.append(c)
pre_c = c
pred_labels.append(no_repeat_blank_label)
for i, label in enumerate(pred_labels):
lb = ""
for i in label:
lb += CHARS[i]
labels.append(lb)
return labels, np.array(pred_labels)
def detectLicence(self,img,x,y):
# 格式转变,BGRtoRGB
input = img
bboxes = create_mtcnn_net(input, (50, 15), self.device, p_model_path='MTCNN/weights/pnet_Weights', o_model_path='MTCNN/weights/onet_Weights')
for i in range(bboxes.shape[0]):
bbox = bboxes[i, :4]
x1, y1, x2, y2 = [int(bbox[j]) for j in range(4)]
w = int(x2 - x1 + 1.0)
h = int(y2 - y1 + 1.0)
img_box = np.zeros((h, w, 3))
t1=(y1-y2)/10
t2=(x2-x1)/10
xyxy=[x1,y1,x2,y2]
img_box = img[y1:y2, x1:x2, :]
if img_box is None:
continue
try:
im = cv2.resize(img_box, (94, 24), interpolation=cv2.INTER_CUBIC)
except:
continue
im = (np.transpose(np.float32(im), (2, 0, 1)) - 127.5)*0.0078125
data = torch.from_numpy(im).float().unsqueeze(0).to(self.device) # torch.Size([1, 3, 24, 94])
transfer = self.STN(data)
preds = self.lprnet(transfer)
transformed_img = convert_image(transfer)
preds = preds.cpu().detach().numpy() # (1, 68, 18)
labels, pred_labels = self.decode(preds, self.CHARS)
# draw.rectangle(
# [x1,y1,x2,y2],outline=(0,0,255))
#if(labels!=None):
if re.match(r'^[\u4e00-\u9fa5][A-Z0-9]{6}$',labels[0]) != None:
#在图片上绘制中文
pass
#plot_one_box(xyxy, img, label=labels[0], color=Color.YELLOW, line_thickness=3)
else:
labels[0]=""
#xyxy=[x+x1,y+y1,x+x2,y+y2]
#plot_one_box(xyxy, img, label=labels[0], color=(100,205,255), line_thickness=3)
return xyxy,labels[0]
#return xyxy,labels[0]